spaCy/spacy/pipeline.pyx

825 lines
29 KiB
Cython

# cython: infer_types=True
# cython: profile=True
# coding: utf8
from __future__ import unicode_literals
import numpy
cimport numpy as np
import cytoolz
from collections import OrderedDict
import ujson
import msgpack
from thinc.api import chain
from thinc.v2v import Affine, Softmax
from thinc.t2v import Pooling, max_pool, mean_pool
from thinc.neural.util import to_categorical, copy_array
from thinc.neural._classes.difference import Siamese, CauchySimilarity
from .tokens.doc cimport Doc
from .syntax.nn_parser cimport Parser
from .syntax import nonproj
from .syntax.ner cimport BiluoPushDown
from .syntax.arc_eager cimport ArcEager
from .morphology cimport Morphology
from .vocab cimport Vocab
from .syntax import nonproj
from .compat import json_dumps
from .attrs import POS
from .parts_of_speech import X
from ._ml import Tok2Vec, build_text_classifier, build_tagger_model
from ._ml import link_vectors_to_models
from . import util
class SentenceSegmenter(object):
"""A simple spaCy hook, to allow custom sentence boundary detection logic
(that doesn't require the dependency parse). To change the sentence
boundary detection strategy, pass a generator function `strategy` on
initialization, or assign a new strategy to the .strategy attribute.
Sentence detection strategies should be generators that take `Doc` objects
and yield `Span` objects for each sentence.
"""
name = 'sbd'
def __init__(self, vocab, strategy=None):
self.vocab = vocab
if strategy is None or strategy == 'on_punct':
strategy = self.split_on_punct
self.strategy = strategy
def __call__(self, doc):
doc.user_hooks['sents'] = self.strategy
return doc
@staticmethod
def split_on_punct(doc):
start = 0
seen_period = False
for i, word in enumerate(doc):
if seen_period and not word.is_punct:
yield doc[start:word.i]
start = word.i
seen_period = False
elif word.text in ['.', '!', '?']:
seen_period = True
if start < len(doc):
yield doc[start:len(doc)]
class Pipe(object):
"""This class is not instantiated directly. Components inherit from it, and
it defines the interface that components should follow to function as
components in a spaCy analysis pipeline.
"""
name = None
@classmethod
def Model(cls, *shape, **kwargs):
"""Initialize a model for the pipe."""
raise NotImplementedError
def __init__(self, vocab, model=True, **cfg):
"""Create a new pipe instance."""
raise NotImplementedError
def __call__(self, doc):
"""Apply the pipe to one document. The document is
modified in-place, and returned.
Both __call__ and pipe should delegate to the `predict()`
and `set_annotations()` methods.
"""
scores = self.predict([doc])
self.set_annotations([doc], scores)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
"""Apply the pipe to a stream of documents.
Both __call__ and pipe should delegate to the `predict()`
and `set_annotations()` methods.
"""
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
scores = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
def predict(self, docs):
"""Apply the pipeline's model to a batch of docs, without
modifying them.
"""
raise NotImplementedError
def set_annotations(self, docs, scores):
"""Modify a batch of documents, using pre-computed scores."""
raise NotImplementedError
def update(self, docs, golds, drop=0., sgd=None, losses=None):
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model.
Delegates to predict() and get_loss().
"""
raise NotImplementedError
def get_loss(self, docs, golds, scores):
"""Find the loss and gradient of loss for the batch of
documents and their predicted scores."""
raise NotImplementedError
def add_label(self, label):
"""Add an output label, to be predicted by the model.
It's possible to extend pre-trained models with new labels,
but care should be taken to avoid the "catastrophic forgetting"
problem.
"""
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
"""Initialize the pipe for training, using data exampes if available.
If no model has been initialized yet, the model is added."""
if self.model is True:
self.model = self.Model(**self.cfg)
link_vectors_to_models(self.vocab)
def use_params(self, params):
"""Modify the pipe's model, to use the given parameter values."""
with self.model.use_params(params):
yield
def to_bytes(self, **exclude):
"""Serialize the pipe to a bytestring."""
serialize = OrderedDict()
serialize['cfg'] = lambda: json_dumps(self.cfg)
if self.model in (True, False, None):
serialize['model'] = lambda: self.model
else:
serialize['model'] = self.model.to_bytes
serialize['vocab'] = self.vocab.to_bytes
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
"""Load the pipe from a bytestring."""
def load_model(b):
if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(**self.cfg)
self.model.from_bytes(b)
deserialize = OrderedDict((
('cfg', lambda b: self.cfg.update(ujson.loads(b))),
('vocab', lambda b: self.vocab.from_bytes(b)),
('model', load_model),
))
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, **exclude):
"""Serialize the pipe to disk."""
serialize = OrderedDict()
serialize['cfg'] = lambda p: p.open('w').write(json_dumps(self.cfg))
serialize['vocab'] = lambda p: self.vocab.to_disk(p)
if self.model not in (None, True, False):
serialize['model'] = lambda p: p.open('wb').write(self.model.to_bytes())
util.to_disk(path, serialize, exclude)
def from_disk(self, path, **exclude):
"""Load the pipe from disk."""
def load_model(p):
if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(**self.cfg)
self.model.from_bytes(p.open('rb').read())
deserialize = OrderedDict((
('cfg', lambda p: self.cfg.update(_load_cfg(p))),
('vocab', lambda p: self.vocab.from_disk(p)),
('model', load_model),
))
util.from_disk(path, deserialize, exclude)
return self
def _load_cfg(path):
if path.exists():
return ujson.load(path.open())
else:
return {}
class Tensorizer(Pipe):
"""Assign position-sensitive vectors to tokens, using a CNN or RNN."""
name = 'tensorizer'
@classmethod
def Model(cls, width=128, embed_size=4000, **cfg):
"""Create a new statistical model for the class.
width (int): Output size of the model.
embed_size (int): Number of vectors in the embedding table.
**cfg: Config parameters.
RETURNS (Model): A `thinc.neural.Model` or similar instance.
"""
width = util.env_opt('token_vector_width', width)
embed_size = util.env_opt('embed_size', embed_size)
return Tok2Vec(width, embed_size, **cfg)
def __init__(self, vocab, model=True, **cfg):
"""Construct a new statistical model. Weights are not allocated on
initialisation.
vocab (Vocab): A `Vocab` instance. The model must share the same
`Vocab` instance with the `Doc` objects it will process.
model (Model): A `Model` instance or `True` allocate one later.
**cfg: Config parameters.
EXAMPLE:
>>> from spacy.pipeline import TokenVectorEncoder
>>> tok2vec = TokenVectorEncoder(nlp.vocab)
>>> tok2vec.model = tok2vec.Model(128, 5000)
"""
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
self.cfg.setdefault('cnn_maxout_pieces', 3)
def __call__(self, doc):
"""Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
model. Vectors are set to the `Doc.tensor` attribute.
docs (Doc or iterable): One or more documents to add vectors to.
RETURNS (dict or None): Intermediate computations.
"""
tokvecses = self.predict([doc])
self.set_annotations([doc], tokvecses)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
"""Process `Doc` objects as a stream.
stream (iterator): A sequence of `Doc` objects to process.
batch_size (int): Number of `Doc` objects to group.
n_threads (int): Number of threads.
YIELDS (iterator): A sequence of `Doc` objects, in order of input.
"""
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
tokvecses = self.predict(docs)
self.set_annotations(docs, tokvecses)
yield from docs
def predict(self, docs):
"""Return a single tensor for a batch of documents.
docs (iterable): A sequence of `Doc` objects.
RETURNS (object): Vector representations for each token in the docs.
"""
tokvecs = self.model(docs)
return tokvecs
def set_annotations(self, docs, tokvecses):
"""Set the tensor attribute for a batch of documents.
docs (iterable): A sequence of `Doc` objects.
tokvecs (object): Vector representation for each token in the docs.
"""
for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)
doc.tensor = tokvecs
def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
"""Update the model.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
"""
if isinstance(docs, Doc):
docs = [docs]
tokvecs, bp_tokvecs = self.model.begin_update(docs, drop=drop)
return tokvecs, bp_tokvecs
def get_loss(self, docs, golds, scores):
# TODO: implement
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
"""Allocate models, pre-process training data and acquire a trainer and
optimizer.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(**self.cfg)
link_vectors_to_models(self.vocab)
class Tagger(Pipe):
name = 'tagger'
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
self.cfg.setdefault('cnn_maxout_pieces', 2)
self.cfg.setdefault('pretrained_dims',
self.vocab.vectors.data.shape[1])
@property
def labels(self):
return self.cfg.setdefault('tag_names', [])
@labels.setter
def labels(self, value):
self.cfg['tag_names'] = value
def __call__(self, doc):
tags = self.predict([doc])
self.set_annotations([doc], tags)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
tag_ids = self.predict(docs)
self.set_annotations(docs, tag_ids)
yield from docs
def predict(self, docs):
scores = self.model(docs)
scores = self.model.ops.flatten(scores)
guesses = scores.argmax(axis=1)
if not isinstance(guesses, numpy.ndarray):
guesses = guesses.get()
guesses = self.model.ops.unflatten(guesses, [len(d) for d in docs])
return guesses
def set_annotations(self, docs, batch_tag_ids):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
cdef Vocab vocab = self.vocab
tags = list(self.labels)
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
if hasattr(doc_tag_ids, 'get'):
doc_tag_ids = doc_tag_ids.get()
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber preset POS tags
if doc.c[j].tag == 0 and doc.c[j].pos == 0:
vocab.morphology.assign_tag(&doc.c[j], tags[tag_id])
idx += 1
doc.is_tagged = True
def update(self, docs, golds, drop=0., sgd=None, losses=None):
if losses is not None and self.name not in losses:
losses[self.name] = 0.
tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
bp_tag_scores(d_tag_scores, sgd=sgd)
if losses is not None:
losses[self.name] += loss
def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
tag_index = {tag: i
for i, tag in enumerate(self.vocab.morphology.tag_names)}
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype='i')
guesses = scores.argmax(axis=1)
for gold in golds:
for tag in gold.tags:
if tag is None:
correct[idx] = guesses[idx]
else:
correct[idx] = tag_index[tag]
idx += 1
correct = self.model.ops.xp.array(correct, dtype='i')
d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
d_scores /= d_scores.shape[0]
loss = (d_scores**2).sum()
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
return float(loss), d_scores
def begin_training(self, gold_tuples=tuple(), pipeline=None):
orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = {}
for raw_text, annots_brackets in gold_tuples:
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
for tag in tags:
if tag in orig_tag_map:
new_tag_map[tag] = orig_tag_map[tag]
else:
new_tag_map[tag] = {POS: X}
cdef Vocab vocab = self.vocab
if new_tag_map:
vocab.morphology = Morphology(vocab.strings, new_tag_map,
vocab.morphology.lemmatizer,
exc=vocab.morphology.exc)
if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
link_vectors_to_models(self.vocab)
@classmethod
def Model(cls, n_tags, **cfg):
return build_tagger_model(n_tags, **cfg)
def add_label(self, label):
if label in self.labels:
return 0
smaller = self.model[-1]._layers[-1]
larger = Softmax(len(self.labels)+1, smaller.nI)
copy_array(larger.W[:smaller.nO], smaller.W)
copy_array(larger.b[:smaller.nO], smaller.b)
self.model[-1]._layers[-1] = larger
self.labels.append(label)
return 1
def use_params(self, params):
with self.model.use_params(params):
yield
def to_bytes(self, **exclude):
serialize = OrderedDict()
if self.model in (None, True, False):
serialize['model'] = lambda: self.model
else:
serialize['model'] = self.model.to_bytes
serialize['vocab'] = self.vocab.to_bytes
serialize['tag_map'] = lambda: msgpack.dumps(
self.vocab.morphology.tag_map, use_bin_type=True, encoding='utf8')
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
def load_model(b):
if self.model is True:
token_vector_width = util.env_opt(
'token_vector_width',
self.cfg.get('token_vector_width', 128))
self.model = self.Model(self.vocab.morphology.n_tags,
**self.cfg)
self.model.from_bytes(b)
def load_tag_map(b):
tag_map = msgpack.loads(b, encoding='utf8')
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
deserialize = OrderedDict((
('vocab', lambda b: self.vocab.from_bytes(b)),
('tag_map', load_tag_map),
('model', lambda b: load_model(b)),
))
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, **exclude):
self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
serialize = OrderedDict((
('vocab', lambda p: self.vocab.to_disk(p)),
('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
self.vocab.morphology.tag_map,
use_bin_type=True,
encoding='utf8'))),
('model', lambda p: p.open('wb').write(self.model.to_bytes())),
('cfg', lambda p: p.open('w').write(json_dumps(self.cfg)))
))
util.to_disk(path, serialize, exclude)
def from_disk(self, path, **exclude):
def load_model(p):
if self.model is True:
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
self.model.from_bytes(p.open('rb').read())
def load_tag_map(p):
with p.open('rb') as file_:
tag_map = msgpack.loads(file_.read(), encoding='utf8')
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
deserialize = OrderedDict((
('cfg', lambda p: self.cfg.update(_load_cfg(p))),
('vocab', lambda p: self.vocab.from_disk(p)),
('tag_map', load_tag_map),
('model', load_model),
))
util.from_disk(path, deserialize, exclude)
return self
class MultitaskObjective(Tagger):
"""Experimental: Assist training of a parser or tagger, by training a
side-objective.
"""
name = 'nn_labeller'
def __init__(self, vocab, model=True, target='dep_tag_offset', **cfg):
self.vocab = vocab
self.model = model
if target == 'dep':
self.make_label = self.make_dep
elif target == 'tag':
self.make_label = self.make_tag
elif target == 'ent':
self.make_label = self.make_ent
elif target == 'dep_tag_offset':
self.make_label = self.make_dep_tag_offset
elif target == 'ent_tag':
self.make_label = self.make_ent_tag
elif hasattr(target, '__call__'):
self.make_label = target
else:
raise ValueError("MultitaskObjective target should be function or "
"one of: dep, tag, ent, dep_tag_offset, ent_tag.")
self.cfg = dict(cfg)
self.cfg.setdefault('cnn_maxout_pieces', 2)
self.cfg.setdefault('pretrained_dims',
self.vocab.vectors.data.shape[1])
@property
def labels(self):
return self.cfg.setdefault('labels', {})
@labels.setter
def labels(self, value):
self.cfg['labels'] = value
def set_annotations(self, docs, dep_ids):
pass
def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=None):
gold_tuples = nonproj.preprocess_training_data(gold_tuples)
for raw_text, annots_brackets in gold_tuples:
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
for i in range(len(ids)):
label = self.make_label(i, words, tags, heads, deps, ents)
if label is not None and label not in self.labels:
self.labels[label] = len(self.labels)
if self.model is True:
token_vector_width = util.env_opt('token_vector_width')
self.model = chain(
tok2vec,
Softmax(len(self.labels), token_vector_width)
)
link_vectors_to_models(self.vocab)
@classmethod
def Model(cls, n_tags, tok2vec=None, **cfg):
return build_tagger_model(n_tags, tok2vec=tok2vec, **cfg)
def get_loss(self, docs, golds, scores):
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype='i')
guesses = scores.argmax(axis=1)
for gold in golds:
for i in range(len(gold.labels)):
label = self.make_label(i, gold.words, gold.tags, gold.heads,
gold.labels, gold.ents)
if label is None or label not in self.labels:
correct[idx] = guesses[idx]
else:
correct[idx] = self.labels[label]
idx += 1
correct = self.model.ops.xp.array(correct, dtype='i')
d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
d_scores /= d_scores.shape[0]
loss = (d_scores**2).sum()
return float(loss), d_scores
@staticmethod
def make_dep(i, words, tags, heads, deps, ents):
if deps[i] is None or heads[i] is None:
return None
return deps[i]
@staticmethod
def make_tag(i, words, tags, heads, deps, ents):
return tags[i]
@staticmethod
def make_ent(i, words, tags, heads, deps, ents):
if ents is None:
return None
return ents[i]
@staticmethod
def make_dep_tag_offset(i, words, tags, heads, deps, ents):
if deps[i] is None or heads[i] is None:
return None
offset = heads[i] - i
offset = min(offset, 2)
offset = max(offset, -2)
return '%s-%s:%d' % (deps[i], tags[i], offset)
@staticmethod
def make_ent_tag(i, words, tags, heads, deps, ents):
if ents is None or ents[i] is None:
return None
else:
return '%s-%s' % (tags[i], ents[i])
class SimilarityHook(Pipe):
"""
Experimental: A pipeline component to install a hook for supervised
similarity into `Doc` objects. Requires a `Tensorizer` to pre-process
documents. The similarity model can be any object obeying the Thinc `Model`
interface. By default, the model concatenates the elementwise mean and
elementwise max of the two tensors, and compares them using the
Cauchy-like similarity function from Chen (2013):
>>> similarity = 1. / (1. + (W * (vec1-vec2)**2).sum())
Where W is a vector of dimension weights, initialized to 1.
"""
name = 'similarity'
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
@classmethod
def Model(cls, length):
return Siamese(Pooling(max_pool, mean_pool), CauchySimilarity(length))
def __call__(self, doc):
"""Install similarity hook"""
doc.user_hooks['similarity'] = self.predict
return doc
def pipe(self, docs, **kwargs):
for doc in docs:
yield self(doc)
def predict(self, doc1, doc2):
return self.model.predict([(doc1, doc2)])
def update(self, doc1_doc2, golds, sgd=None, drop=0.):
sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
def begin_training(self, _=tuple(), pipeline=None):
"""Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
if self.model is True:
self.model = self.Model(pipeline[0].model.nO)
link_vectors_to_models(self.vocab)
class TextCategorizer(Pipe):
name = 'textcat'
@classmethod
def Model(cls, nr_class=1, width=64, **cfg):
return build_text_classifier(nr_class, width, **cfg)
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
@property
def labels(self):
return self.cfg.setdefault('labels', ['LABEL'])
@labels.setter
def labels(self, value):
self.cfg['labels'] = value
def __call__(self, doc):
scores = self.predict([doc])
self.set_annotations([doc], scores)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
scores = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
def predict(self, docs):
scores = self.model(docs)
scores = self.model.ops.asarray(scores)
return scores
def set_annotations(self, docs, scores):
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
doc.cats[label] = float(scores[i, j])
def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
scores, bp_scores = self.model.begin_update(docs, drop=drop)
loss, d_scores = self.get_loss(docs, golds, scores)
bp_scores(d_scores, sgd=sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += loss
def get_loss(self, docs, golds, scores):
truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
not_missing = numpy.ones((len(golds), len(self.labels)), dtype='f')
for i, gold in enumerate(golds):
for j, label in enumerate(self.labels):
if label in gold.cats:
truths[i, j] = gold.cats[label]
else:
not_missing[i, j] = 0.
truths = self.model.ops.asarray(truths)
not_missing = self.model.ops.asarray(not_missing)
d_scores = (scores-truths) / scores.shape[0]
d_scores *= not_missing
mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
return mean_square_error, d_scores
def add_label(self, label):
if label in self.labels:
return 0
smaller = self.model[-1]._layers[-1]
larger = Affine(len(self.labels)+1, smaller.nI)
copy_array(larger.W[:smaller.nO], smaller.W)
copy_array(larger.b[:smaller.nO], smaller.b)
self.model[-1]._layers[-1] = larger
self.labels.append(label)
return 1
def begin_training(self, gold_tuples=tuple(), pipeline=None):
if pipeline and getattr(pipeline[0], 'name', None) == 'tensorizer':
token_vector_width = pipeline[0].model.nO
else:
token_vector_width = 64
if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(len(self.labels), token_vector_width,
**self.cfg)
link_vectors_to_models(self.vocab)
cdef class DependencyParser(Parser):
name = 'parser'
TransitionSystem = ArcEager
@property
def postprocesses(self):
return [nonproj.deprojectivize]
def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
for target in []:
labeller = MultitaskObjective(self.vocab, target=target)
tok2vec = self.model[0]
labeller.begin_training(gold_tuples, pipeline=pipeline,
tok2vec=tok2vec)
pipeline.append(labeller)
self._multitasks.append(labeller)
def __reduce__(self):
return (DependencyParser, (self.vocab, self.moves, self.model),
None, None)
cdef class EntityRecognizer(Parser):
name = 'ner'
TransitionSystem = BiluoPushDown
nr_feature = 6
def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
for target in []:
labeller = MultitaskObjective(self.vocab, target=target)
tok2vec = self.model[0]
labeller.begin_training(gold_tuples, pipeline=pipeline,
tok2vec=tok2vec)
pipeline.append(labeller)
self._multitasks.append(labeller)
def __reduce__(self):
return (EntityRecognizer, (self.vocab, self.moves, self.model),
None, None)
__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer']